Complementarities in Production Technologies: An Empirical Analysis of the Dairy Industry
Bibliographic record
Abstract
In this article, we present empirical evidence to show that a commonly held belief is likely false. Specifically, we examine the claim that three widely used dairy technologies and management practices complement the use of rbST in the sense that they increase the marginal return of rbST. Using the definition described in Milgrom and Roberts (1990) that the presence of supermodular profit or total output functions is evidence of complementarity, our results show that the use of a computerized feeding system or total mixed ration feed balance system is complementary with the use of rbST, but that this complementarity only exists for when considering the effects on feed costs per cow. We are unable to detect any complementary relationships for operating margins per hundredweight of milk, operating margins per cow, or feed costs per hundredweight of milk. These results show that having a TMR feed balance system, being a member in a DHIA, or using a computerized feed system do not necessarily increase the marginal productivity or profitability of using rbST. This paper is the first to our knowledge that uses the production function approach to estimate econometrically whether complementarities among dairy technologies exist.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".